A Survey on Multi-View Clustering

Abstract

With advances in information acquisition technologies, multi-view data become ubiquitous. Multi-view learning has thus become more and more popular in machine learning and data mining fields. Multi-view unsupervised or semi-supervised learning, such as co-training, co-regularization has gained considerable attention. Although recently, multi-view clustering (MVC) methods have been developed rapidly, there has not been a survey to summarize and analyze the current progress. Therefore, this paper reviews the common strategies for combining multiple views of data and based on this summary we propose a novel taxonomy of the MVC approaches. We further discuss the relationships between MVC and multi-view representation, ensemble clustering, multi-task clustering, multi-view supervised and semi-supervised learning. Several representative real-world applications are elaborated. To promote future development of MVC, we envision several open problems that may require further investigation and thorough examination.

17 pages, 4 figures

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• Doubly Aligned Incomplete Multi-view Clustering
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• Multiple Partitions Aligned Clustering
• Improving Image Clustering With Multiple Pretrained CNN Feature Extractors
• Generative Partial Multi-View Clustering
• Multiple Kernel $k$-Means Clustering by Selecting Representative Kernels
• One-Pass Incomplete Multi-view Clustering
• mvlearn: Multiview Machine Learning in Python
• Dialog Intent Induction with Deep Multi-View Clustering
• Multi-view Hierarchical Clustering
• Multi-view Subspace Clustering Networks with Local and Global Graph Information
• A multilevel clustering technique for community detection
• Tensor-based Intrinsic Subspace Representation Learning for Multi-view Clustering
• Consistency-aware and Inconsistency-aware Graph-based Multi-view Clustering
• Quantizing Multiple Sources to a Common Cluster Center: An Asymptotic Analysis
• Locality Relationship Constrained Multi-view Clustering Framework
• Ordinally Consensus Subset over Multiple Metrics
• Error-Robust Multi-View Clustering: Progress, Challenges and Opportunities
• Consistent and Complementary Graph Regularized Multi-view Subspace Clustering
• Multi-view Subspace Clustering via Partition Fusion